Vacation rental marketplaces in which disparate owners of second or vacation homes have experienced increasing growth recently. In a vacation rental marketplace, a family or a group of people (e.g., group of friends) may rent anything from cabins, condominiums, summer homes, to villas, barns, farm houses, and castles. These types of rental properties are desirable as typical hotel or motel buildings are not well-suited to accommodate families or groups of people, and are usually less private and less comforting to some guests.
With the advent of networked computing devices, the computer-based renting of properties electronically has enabled travelers to more readily enjoy the experiences of renting others' homes. However, some conventional techniques and known technological approaches to renting properties via computer networks typically employ an increasing numbers of computing systems (e.g., hundreds or thousands of computing devices, including servers and databases) over which those computing systems are distributed conventionally or arranged using typical sharded database schemas. Further, increasing amounts of data are being processed by and among numerous and disparate networked computing devices, which, in turn, complicates and hinders error detection and resolution through conventional trouble-shooting techniques.
While conventional trouble-shooting techniques are functional, some known approaches to detecting and resolving errors in data stream communications are less than optimal when applied to computing systems consuming greater amounts of data at faster transmission rates and requiring greater throughput. In particular, some conventional approaches to error detection in high-speed data channels having high-throughput are not well-suited to archive large numbers of attributes to determine historically whether a particular data communication channel is operating normally or abnormally.
As such, common approaches to error detection and resolution may be relatively costly in terms of computing resources, manpower, and capital. Thus, an entity (e.g., a corporate entity) may experience degradation of performance in the operability of its computing resources, which, in turn, reduces the efficacy of serving data, such as webpages, to prospective consumers of data. For example, the entity may specify a range of acceptable response times (e.g., in accordance with a service level agreement, or SLA) to provide a specific response time to a computing device based on criteria set forth in inquires to a distributed computing system. Typically, conventional error detection is not well-suited to detect spikes or abnormalities in response times (e.g., in real-time). As such, conventional error detection techniques are suboptimal in identifying abnormalities in the performance of data streams within distributed computing systems. Thus, users may experience relatively “slow” data accesses that may be perceived as delays in network or communications.
Thus, what is needed is a solution for anomaly detection and resolution in a data stream processor disposed in a distributed computerized rental system that processes numerous data streams, without the limitations of conventional techniques.